Discover datasets

This dataset comprises electroencephalographic recordings from 12 healthy subjects during music listening with and without a passive head-mounted display (PHMD). The study employed a 16-channel EEG system sampling at 512 Hz. A passive head-mounted display is a head-mounted device without integrated electronics, consisting of a smartphone holder. The dataset provides raw, unfiltered EEG data in mat and csv formats, designed to support research on the effects of head-mounted displays and auditory stimuli on brain activity. This is a BIDS-converted derivative of the original Zenodo dataset (10.5281/zenodo.2617084).

Participants

12

Channels

16 (10-10)

Citations

19

HED

v8.4.0

Size

516 MB

Version

v1.0.5

Updated

Aug 18, 2026

BigP3BCI Study J is a P300-based brain-computer interface dataset comprising EEG recordings from 20 healthy subjects performing a 9x8 character grid speller task. This derivative dataset is part of the larger BigP3BCI collection, the largest public P300 BCI dataset with ~267 subjects across 20 studies. The data were acquired at 256 Hz using 16-channel EEG with a g.USBamp amplifier and include target and non-target event classifications suitable for machine learning applications.

Participants

20

Channels

16 (10-10)

HED

v8.4.0

Size

1.49 GB

Version

v1.0.3

Updated

Aug 18, 2026

A large-scale longitudinal dataset of sensorimotor rhythm-based brain-computer interface (BCI) training in 62 healthy adults. The dataset comprises over 600 hours of EEG recordings across 598 sessions with more than 250,000 trials of motor imagery tasks (left hand, right hand, both hands, and rest). This resource enables investigation of BCI learning dynamics and algorithm development for non-invasive neural control applications.

Participants

62

Channels

60 (10-10)

Citations

59

HED

v8.4.0

Size

722 GB

Version

v1.0.4

Updated

Aug 18, 2026

A comprehensive EEG dataset comprising 54 healthy subjects performing three major brain-computer interface (BCI) paradigms: motor imagery (MI), event-related potential (ERP), and steady-state visually evoked potential (SSVEP) across two sessions. The dataset investigates BCI illiteracy rates and performance variations, revealing that while MI showed the highest illiteracy rate (53.7%), all participants could control at least one BCI paradigm. Data were acquired at 1000 Hz using 62 EEG channels with concurrent electromyography recordings.

Participants

54

Channels

62 (10-05)

Citations

99

HED

v8.4.0

Size

121 GB

Version

v1.0.5

Updated

Aug 18, 2026

BigP3BCI Study R is a P300-based brain-computer interface dataset comprising EEG recordings from 20 subjects performing a 9x8 multi-face character grid speller task across two sessions. This derivative dataset is part of the larger BigP3BCI collection, the largest public P300 BCI dataset with ~267 subjects across 20 studies. The data were acquired at 256 Hz using 32-channel EEG with a g.USBamp amplifier and are formatted according to BIDS standards with HED event annotations for standardized analysis and machine learning applications.

Participants

20

Channels

32 (10-10)

HED

v8.4.0

Size

6.59 GB

Version

v1.0.3

Updated

Aug 18, 2026

An open-access electroencephalography dataset of motor imagery brain-computer interface performance under six different distraction conditions. Sixteen healthy participants performed left versus right hand motor imagery while experiencing flickering video, number search tasks, news listening, eyes closed state, vibro-tactile stimulation, or no distraction. The dataset comprises 504 trials per subject recorded at 1000 Hz with 63 EEG channels, including one calibration run without feedback and six feedback runs with distinct distraction manipulations.

Participants

16

Channels

63 (10-05)

Citations

13

HED

v8.4.0

Size

72.1 GB

Version

v1.0.7

Updated

Aug 18, 2026

BigP3BCI Study C is a P300-based brain-computer interface dataset comprising EEG recordings from 19 healthy subjects performing a visual speller task using a 6x6 checkerboard paradigm. The dataset contains single-session recordings with 32-channel EEG data sampled at 256 Hz, annotated with target and non-target event labels. This derivative dataset is part of the larger BigP3BCI collection, the largest public P300 BCI dataset, and is formatted according to BIDS standards with HED event annotations for machine learning applications.

Participants

19

Channels

32 (10-10)

HED

v8.4.0

Size

4.11 GB

Version

v1.0.3

Updated

Aug 18, 2026

This dataset comprises EEG recordings from 54 healthy participants performing a P300-based brain-computer interface speller task, designed to investigate BCI illiteracy. The study includes 62 EEG channels and 4 EMG channels sampled at 1000 Hz, with participants completing offline training and online test phases using a 36-symbol row-column speller paradigm. The dataset achieved 96.7% accuracy with an 11.1% BCI illiteracy rate, providing a comprehensive resource for evaluating P300-based BCI performance and individual differences in BCI competence.

Participants

54

Channels

62 (10-05)

Citations

100

HED

v8.4.0

Size

132 GB

Version

v1.0.4

Updated

Aug 18, 2026

BigP3BCI Study Q is a P300-based brain-computer interface dataset comprising EEG recordings from 36 ALS subjects across 3 sessions each, using a 6x6 color intensification speller paradigm. The dataset contains 32-channel EEG data sampled at 256 Hz with standardized 10-20 electrode montage, annotated with target and non-target event labels using HED 8.4.0 schema. This derivative dataset is part of the larger BigP3BCI collection, the largest public P300 BCI dataset, and is optimized for machine learning applications in brain-computer interface research.

Participants

36

Channels

32 (10-10)

HED

v8.4.0

Size

3.70 GB

Version

v1.0.3

Updated

Aug 18, 2026

BigP3BCI Study S2 is a P300-based brain-computer interface dataset comprising EEG recordings from 24 healthy subjects performing a 9x8 house/tool visual speller paradigm. The dataset contains 32-channel EEG data sampled at 256 Hz, annotated with target and non-target event classifications using HED 8.4.0 schema. This dataset is part of the larger BigP3BCI collection (the largest public P300 BCI dataset with recordings from approximately 267 subjects across 20 studies), designed to support machine learning research and BCI benchmarking. Trial intervals span 0-1.0 seconds from stimulus onset.

Participants

24

Channels

32 (10-10)

HED

v8.4.0

Size

3.74 GB

Version

v1.0.3

Updated

Aug 18, 2026